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6th International conference on Next Generation Data-Driven networks, NGDN 2024

作者： He, Xu Xu, Qihong Luo, Huan Fuzhou University College of Computer and Data Science Fuzhou China Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9798350388374

With the continuous development of intelligent transportation technologies such as autonomous driving and navigation, accurate perception of road markings becomes crucial. However, due to limitations in sensor perspectives and obstacles blocking the view, the scanned point cloud of road markings is often incomplete, potentially leading to erroneous decisions in intelligent transportation systems. Therefore, it becomes imperative to recover complete road markings from these incomplete point clouds. This paper proposes a text-guided road marking completion method, which integrates text information with point cloud data using attention mechanisms. By leveraging text information to guide the network in completing road marking point clouds, the proposed method aims to enhance the perception accuracy and completeness of road markings. Experimental validation on road marking datasets demonstrates the effectiveness and feasibility of the proposed approach. © 2024 IEEE.

关键词： Road and street markings

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An Optimized sensors Associated Forest Fire Prediction and Reporting System Based on Internet of Things Technology

An Optimized Sensors Associated Forest Fire Prediction and R...

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2024 International conference on Innovative Computing, intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Deepak, P. Srinu, Nidamanuri Xavier, Benisha Maria Sravani, R. Nandurkar, Y. Afaneh, Suha Einstein Engineering college Dept of Computer Science and Engineering Tirunelveli India Qis College of Engineering and Technology Department of Cse Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Jeppiaar Institute of Technology Department of Ece Kunnam Tamilnadu Chennai India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh Prakasam District523272 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa university Faculty of information technology Department of cybersecurity Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Forest fires pose a significant threat to the environment, leading to the destruction of ecosystems and contributing to climate change. This paper presents an optimized sensor-based forest fire prediction and reporting system leveraging Internet of Things (IoT) technology. The system integrates Wireless sensor networks (WSNs) and IoT-enabled devices to monitor environmental parameters, including temperature, CO2, and smoke levels. The system demonstrated high sensor accuracy, with temperature sensors showing a maximum deviation of ±0.5°C and gas sensors displaying an accuracy of ±2 ppm for CO2. Smoke detection accuracy was within ±0.05 ppm, making the system reliable for early fire detection. The system's response time improved significantly with increasing temperatures, detecting fire events within 10 seconds for temperatures above 100°C. The Wireless sensor Network maintained a packet delivery ratio (PDR) of over 99% and an average latency of 200ms under high node density, ensuring timely transmission of critical data. With power consumption optimized for long-term deployment, sensor nodes were estimated to operate for up to 24 months. Overall, this system offers a cost-effective, accurate, and efficient solution for real-time forest fire monitoring and response, with potential applications in large, remote forest areas. © 2024 IEEE.

关键词： Temperature sensors

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intelligent sensor networks in Industry 5.0. Generalized Concept of Creating Digital Platforms for Managing Complex Systems Based on a Regularizing Bayesian Approach 25

Intelligent Sensor Networks in Industry 5.0. Generalized Con...

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25th International conference on Soft Computing and Measurements, SCM 2022

作者： Prokopchina, Svetlana V. Financial University under the Government of the Russian Federation Moscow Russia

ISBN: (纸本)9781665496698

Within the framework of the Industry 4.0 concept, intensive development of the processes of intellectualization of sensory systems is envisaged. Among the most important specific properties of real measuring processes in complex systems is, first of all, their implementation in conditions of considerable uncertainty. The uncertainty is caused by a priori incompleteness, inaccuracy, vagueness of information about a complex measuring object and its functioning environment, which does not allow us to build an adequate model of the object before conducting a measuring experiment, identify and formalize the influencing factors of the external environment and develop effective algorithms for the functioning of information and measurement *** article proposes an approach to the intellectualization of measurement systems in conditions of uncertainty by creating intelligent sensor networks based on Bayesian intelligent technologies (BITS) and means of their implementation. Typical modules of such networks are considered, which are integrated sets of various sensors and intelligent measurement information processing *** sensor sets may include both physically implemented measuring devices and virtual sensors for measuring non-quantitative or integral characteristics. The results of the work of the networks are comprehensive assessments of the state of complex objects and recommendations for ensuring their sustainable functioning. An important part of such systems is the built-in means of a complete metrological justification of all the solutions obtained. The systems have a hierarchical architecture corresponding to the levels of management of complex objects, which has the possibility of self-development based on newly received information. This is achieved thanks to models and measurement scales with dynamic constraints, on which all algorithms used in these networks are built. © 2022 IEEE.

关键词： sensor networks

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Machine Learning Based Smart Helmet for Air Quality and Hazardous Event Detection in the Mining Industry

Machine Learning Based Smart Helmet for Air Quality and Haza...

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2023 International conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Devi, D. Prasanna, N. Ram, R.A. Prateek Mohamed, N. Niyaz Sri Krishna College of Engineering and Technology Department of Ece Coimbatore India

ISBN: (纸本)9798350300857

Ensuring the safety of underground miners is of utmost importance in the mining industry, given the devastating consequences of mining accidents. To address this concern, this research study has developed an intelligent headgear equipped with an array of sensors to continuously monitor the working conditions of miners. This advanced device is engineered to identify critical parameters such as temperature, humidity, concentrations of harmful gases, and vibrations. It is equipped with dedicated sensors for each of these parameters, and it promptly transmits real-time data to a centralized control centre. The control centre can analyse this information and take immediate actions, which may include worker evacuation or improvements to working conditions. The proposed system utilizes an Arduino microcontroller for processing the data from these sensors and then transmits it via a Wi-Fi module. The proposed model has utilized Cloud-based loT and machine learning algorithms to enhance the accuracy of the sensor readings. Specifically, an Artificial Neural Network (ANN) algorithm is employed to train a dataset of sensor information stored in CS V format. This innovative system has the potential to significantly elevate safety standards and minimize the risk of mining accidents. © 2023 IEEE.

关键词： Neural networks

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Adaptive Neuro-Fuzzy Inference Systems for Cognitive sensor Communication in Smart Cities

Adaptive Neuro-Fuzzy Inference Systems for Cognitive Sensor ...

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2024 IEEE International conference on intelligent Systems and Advanced Applications, ICISAA 2024

作者： Pradhan, Rahul Prathiba, Ch. Kholmukhamedov, Timur Patil, B.N. Mary, S. Suma Christal Raj, I Infant GLA University Department of Computer Engineering and Applications Mathura India Vignana Bharathi Institute of Technology Department of CSM Ghatkesar Hyderabad India Turin Polytechnic University Automatic Control and Computer Engineering Department Tashkent Uzbekistan Yeshwantrao Chavan College of Engineering Department of Applied Physics Nagpur India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K. Ramakrishnan College of Engineering Department of CSE Tamil Nadu Trichy India

ISBN: (纸本)9798331539948

Modern urban development depends on optimizing cognitive sensor communications in smart cities, which is the focus of this investigation. In the era of expanding sensor networks, efficient data transmission is paramount for the success of smart city initiatives. The current state of sensor communication faces challenges like suboptimal adaptability, inefficient energy usage, and limited fault detection. This study proposes a solution through the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS), identifying these challenges as opportunities for improvement. The novelty lies in ANFIS's introduction as an intelligent and adaptive solution. Its ability to learn from sensor data, adapt to changing conditions, and optimize communication parameters sets it apart in enhancing cognitive sensor communication. The research methodology involves a systematic approach, encompassing data collection, pre-processing, and ANFIS model development. The model is rigorously trained, validated, and tested for real-world effectiveness. Integration into the smart city communication system is explored, emphasizing applications in context-aware communication, energy-efficient protocols, and fault detection. Performance is evaluated using metrics like accuracy, reliability, and efficiency, demonstrating the model's efficacy in addressing drawbacks and enhancing overall sensor communication in smart cities. The findings contribute to advancing intelligent systems for urban infrastructure and services. © 2024 IEEE.

关键词： Fuzzy neural networks

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An Energy-Saving MAC Protocol for Underwater Acoustic sensor networks Based on TDMA 2

An Energy-Saving MAC Protocol for Underwater Acoustic Sensor...

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2nd International conference on Signal processing and intelligent Computing, SPIC 2024

作者： Zhang, Xinhao Liang, Ying Shenyang Ligong University School of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350368888

To construct an efficient and reliable marine Internet of Things, there must be effective and dependable communication between underwater nodes. However, the underwater acoustic channel has significant peculiarities, mainly characterized by slow sound propagation speed, spatiotemporal uncertainty, narrow channel bandwidth, time-varying multipath, severe channel fading, exposed and hidden terminals, and limited energy. To address these issues, this paper proposes an energy-saving MAC protocol for three-dimensional environments, aimed at ensuring successful reception of data frames while minimizing collisions to save energy, and at the same time, ensuring a larger throughput and lower latency. This MAC protocol is implemented based on TDMA, divided into three stages. © 2024 IEEE.

关键词： Fading channels

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Location Awareness in Beyond 5G networks via Reconfigurable intelligent Surfaces

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2022年第7期40卷 2011-2025页

作者： Wang, Ziyi Liu, Zhenyu Shen, Yuan Conti, Andrea Win, Moe Z. MIT Wireless Informat & Network Sci Lab Cambridge MA 02139 USA Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Ferrara Dept Engn & CNIT I-44122 Ferrara Italy MIT Lab Informat & Decis Syst LIDS Cambridge MA 02139 USA

Achieving accurate location-awareness in wireless networks requires integrated sensing and communication (ISAC), where optimization, signal processing, and data fusion are performed under a common framework. The efficiency of ISAC in complex wireless environments can be improved via the use of reconfigurable intelligent surfaces (RISs). This paper introduces the concept of continuous intelligent surface (CIS) and establishes the fundamental limits of RIS-aided ISAC systems, specifically, an RIS-aided localization and communication system. In particular, this paper considers two types of RISs, namely CISs and discrete intelligent surfaces (DISs). First, this paper proposes a general signal model for RIS-aided localization and communication valid for both near-field and far-field scenarios, and then theoretical limits on the localization and communication performance are derived. Based on the proposed model, Fisher information analyses of the localization performance in networks with RISs are performed. Numerical results show that RISs with optimized phase responses can improve the received signal-to-noise ratio (SNR) and spectral efficiency of communication, and the localization accuracy significantly.

关键词： Location awareness Wireless communication Scattering Wireless sensor networks sensors Transmitting antennas Directive antennas Beyond 5G integrated sensing and communication network localization reconfigurable intelligent surface Fisher information

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Multi-IRS-Enabled Integrated Sensing and Communications

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第9期72卷 5853-5867页

作者： Fang, Yuan Zhang, Siyao Li, Xinmin Yu, Xianghao Xu, Jie Cui, Shuguang Chinese Univ Hong Kong Future Network Intelligence Inst FNii Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn SSE Shenzhen 518172 Peoples R China Southwest Univ Sci & Technol Sch Comp Sci & Technol Mianyang 621000 Peoples R China Chinese Univ Hong Kong Shenzhen FNii Shenzhen 518172 Peoples R China Chengdu Univ Coll Comp Sci Chengdu 610100 Peoples R China Chinese Univ Hong Kong Shenzhen Guangdong Prov Key Lab Future Networks Intelligenc Shenzhen 518172 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Chinese Univ Hong Kong Shenzhen SSE Shenzhen Peoples R China

This paper studies a multi-intelligent-reflecting-surface-(IRS)-enabled integrated sensing and communications (ISAC) system, in which multiple IRSs are installed to help the base station (BS) provide ISAC services at separate line-of-sight (LoS) blocked areas. We focus on the scenario with semi-passive uniform linear array (ULA) IRSs for sensing, in which each IRS is integrated with dedicated sensors for processing echo signals, and each IRS simultaneously serves one sensing target and multiple communication users (CUs) in its coverage area. We consider two cases with point and extended targets, in which each IRS aims to estimate the target direction-of-arrival (DoA) and the complete target response matrix, respectively. Under this setup, we first derive the closed-form Cram & eacute;r-Rao bounds (CRBs) for parameter estimation under the two target models. Then, we assume that the BS sends combined information and dedicated sensing signals for ISAC, and accordingly consider two different types of CU receivers that can and cannot cancel the interference from dedicated sensing signals. Under this setup, we minimize the maximum CRB at all IRSs, via jointly optimizing the transmit beamformers at the BS and the reflective beamformers at the multiple IRSs, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs, the maximum transmit power constraint at the BS, and the unit-modulus constraints at the multiple IRSs. To tackle the highly non-convex SINR-constrained max-CRB minimization problems, we propose efficient algorithms based on alternating optimization and semi-definite relaxation, to obtain converged solutions. Finally, numerical results are provided to verify the benefits of our proposed designs over various benchmark schemes based on separate or heuristic beamforming designs.

关键词： sensors Array signal processing Interference Wireless communication Wireless sensor networks Signal to noise ratio Estimation Integrated sensing and communications (ISAC) semi-passive intelligent reflecting surfaces (IRS) Cram & eacute r-Rao bound (CRB) joint transmit reflective beamforming

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Adaptive Memory Differential Evolutionary Algorithm for Network Management Using Variants of Dominating Set Models 2

Adaptive Memory Differential Evolutionary Algorithm for Netw...

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2nd International conference of intelligent Methods, Systems and Applications, IMSA 2024

作者： Hedar, Abdel-Rahman Tarek, Fatma El-Zahraa Mohamed, Mostafa El-Sayed, Gamal A. Assiut University Faculty of Computers & Information Department of Computer Science Assiut71526 Egypt Assiut University Faculty of Engineering Department of Electrical Engineering Assiut71515 Egypt Umm Al-Qura University Department of Computer Science in Jamoum Makkah25371 Saudi Arabia

ISBN: (纸本)9798350362633

In wireless sensor networks, where multiple sensors are typically concentrated in a confined area, determining the optimal size of wireless sensors to use for communication and coordination over the network is essential due to concerns about the preservation of energy and battery. In this paper, we introduce new competitive memory-based differential evolution methods to solve wireless sensor network management problems using variants of dominating set models. The problems considered are formulated as constrained optimization problems that are transformed into unconstrained optimization problems using the penalty principle. Then, differential evolution with several memory-based enhanced procedures is designed to find solutions to the reformed optimization problems. Two versions of the proposed Adaptive Memory Differential Evolutionary Algorithm (AMDEA) are designed to deal with the minimum dominating set problem and its variants. The results obtained from the proposed methods show that they are promising compared to some benchmark methods. © 2024 IEEE.

关键词： Constrained optimization

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Enhancing Accuracy in WBANs Through Optimal sensor Positioning and Signal processing: A Systematic Methodology 11

Enhancing Accuracy in WBANs Through Optimal Sensor Positioni...

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11th International conference on Electrical Engineering, Computer Science and Informatics, EECSI 2024

作者： Al Barazanchi, Israa Ibraheem Abdulrahman, Mayasa M. Thabit, Reema Hashim, Wahidah Dalavi, Amol M. Sekhar, Ravi Malaysia College of Engineering University of Warith Al-Anbiyaa Karbala Iraq College of Engineering Computer Engineering Department University of Baghdad Baghdad Iraq Pune Campus Pune412115 India

ISBN: (纸本)9798350355314

Wireless Body Area networks (WBANs) have transformed fitness monitoring by providing continuous real-time data collection from small sensors placed on or inside the human body. Even though WBANs have important benefits, their accuracy and dependability are jeopardized by issues with sensor placement and signal processing. Incorrect sensor positioning can lead to inaccurate information, while noisy surroundings make signal processing more challenging. This focuses on using a single methodical approach to improve sensor placement and enhance signal processing in WBANs. We suggest a comprehensive solution that tackles placement sensitivity and noise reduction by combining anatomical modeling, real-time feedback mechanisms, and innovative machine learning algorithms. Experimental results show a 25% increase in signal quality and a 35% improvement in data precision when compared to conventional techniques. This method not only enhances the dependability of health monitoring systems but also validates their ability to be scaled and their effectiveness in various medical uses. Our results highlight the potential of WBANs to transform health care services, providing more accurate diagnostics and customized treatments. © 2024 IEEE.

关键词： - WBAN machine learning Modeling anatomically Placement of sensors real-time feedback Signals processing

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